Higher Education Student Support Program Expansion: A Decade of Progress and Success for English as an Additional Language (EAL) and International Students
Bibliographic record
Abstract
One method to support English as an additional language (EAL) students in higher education is through the development of an extracurricular support program catered to the specific academic and psychosocial needs of post-secondary EAL students within an individual faculty. In 2009, the Mount Royal University (MRU) EAL Nursing Student Support Program (NSSP) was created to support the EAL student population within MRU's Bachelor of Nursing (BN) faculty. Following over a decade of support program success documented in a series of scholarly publications, this study aims to capture the longitudinal impact of EAL NSSP on the continued success of its alumni within the academic, professional, and personal domains. A hermeneutic approach to phenomenology was used to measure the perceived impact of the student support measures on their professional and personal development. Participant interviews revealed six themes: (a) skills and knowledge obtained from membership in the support program, (b) continued engagement in professional development and leadership opportunities following support program involvement, (c) accomplishments attained following support program membership, (d) future goals, (e) eagerness to help future generations of EAL students, and (f) the need for continued EAL student support. The findings from this study demonstrate the importance of EAL student support in higher education, showcasing the profound, long-term impact that an effective and intentional EAL student support program design can have.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".